A toolbox for analysing and simulating ISM images
Project description
BrightEyes-ISM
A toolbox for analysing and simulating Image Scanning Microscopy (ISM) datasets. The analysis module contains libraries for:
- Adaptive Pixel Reassignment (https://doi.org/10.1038/s41592-018-0291-9)
- Focus-ISM (https://doi.org/10.1038/s41467-022-35333-y)
- Image Deconvolution (https://doi.org/10.1088/1361-6420/accdc5)
- Fourier Ring Correlation (https://doi.org/10.1038/s41467-019-11024-z)
The simulation module contains libraries for:
- Generation of ISM point spread functions (https://doi.org/10.1016/j.cpc.2022.108315)
- Generation of tubulin phantom samples
The dataio module contains libraries for
- Reading the data and metadata from the MCS software (https://github.com/VicidominiLab/BrightEyes-MCS)
Installation
You can install brighteyes-ism
via pip directly from GitHub:
pip install git+https://github.com/VicidominiLab/BrightEyes-ISM
or using the version on PyPI:
pip install brighteyes-ism
It requires the following Python packages
numpy
scipy
matplotlib
scikit-image
scikit-learn
poppy
PyCustomFocus
h5py
tqdm
statsmodels
matplotlib-scalebar
Documentation
You can find an example of usage here:
https://github.com/VicidominiLab/BrightEyes-ISM/tree/main/examples
You can read the manual of this package on Read the Docs:
https://brighteyes-ism.readthedocs.io
Contributing
Contributions are very welcome. Tests can be run with tox, please ensure the coverage at least stays the same before you submit a pull request.
License
Distributed under the terms of the GNU GPL v3.0 license, "BrightEyes-ISM" is free and open source software
Issues
If you encounter any problems, please file an issue along with a detailed description.
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